Uzair et al. [1]} have recently proposed an approach to detect R-peaks using deep learning technique, which outperforms the traditional methods. In that work, a 1-D U-Net [2]} was used to predict the location of R-peaks, from an ECG signal. We have deployed our recently proposed variant of U-net architecture, 1-D MultiResUNet model [3]} with deep supervision [4]} as done in [5]}, to predict the R-peaks from a continuous ECG signal. MultiResUNet model showed improved performance for 2D applications over the traditional U-Net architecture which incorporates multiresolution analysis and deep supervision is proven to improve the performance of deep networks. The probability map predicted from the model is thresholded and further refined by some post-processing. It is expected that R-peaks will be considerably distant from one another. Thus, to minimize false positives, we ignore nearby predicted beats and replace them with the median prediction. To reduce false negatives we empirically select a suitable threshold value.
